Green Symphony: Deep Learning for Crop Health Assessment

Abstract

The agricultural sector holds paramount importance in our economy, impacting our daily lives significantly. Effective management of agricultural resources is crucial for ensuring profitability in crop production. However, farmers often lack expertise in identifying and managing plant leaf diseases, leading to reduced yields. Detecting and classifying leaf diseases is pivotal for maximizing agricultural productivity. Utilizing Convolutional Neural Networks (CNNs) offers a promising solution for automated leaf disease detection and classification. This research focuses on detecting diseases in key crops such as apple, grape, corn, potato, and tomato plants. By leveraging deep CNN models, this study aims to enhance disease monitoring in large crop fields, enabling prompt identification of disease symptoms and facilitating timely intervention. Such advancements in plant leaf disease detection have broad applications in biological research and agricultural institutes, offering immense potential to optimize crop health management and maximize yields. Comparing the proposed deep CNN model with established transfer learning approaches like VGG16 underscores the significance of this research endeavor in addressing the critical need for efficient disease detection and management in agriculture..

Introduction

The project aims to develop a Convolutional Neural Network (CNN)-based system to accurately detect and classify leaf diseases in various crops. It involves collecting a large, diverse dataset of annotated leaf images, designing and training an optimized CNN architecture that handles variations in lighting and background, and evaluating the system for real-time, scalable deployment in agricultural settings. This automated approach addresses the limitations of traditional manual inspection, which is slow, error-prone, and not scalable.

The literature review highlights existing research on CNNs and transfer learning for plant disease detection, discussing various datasets, architectures, and challenges such as dataset scarcity and real-time application.

The proposed system innovates by using image fusion, combining segmented and RGB images to improve classification accuracy. Trained on over 54,000 images across 38 disease classes, this custom CNN model outperforms previous methods. CNNs excel in automatic feature extraction from leaf images, enabling robust differentiation between healthy and diseased leaves without manual feature engineering.

The architecture involves preprocessing the data, training multiple CNN variants (e.g., LeNet, VGG, ResNet), and selecting the best performing model based on testing results. The results demonstrate that CNN-based detection provides early, precise disease identification, empowering farmers to improve crop health and sustainability.

Looking ahead, future advancements may include integrating hyperspectral imaging, drone-based remote sensing, and mobile apps for widespread, real-time disease diagnosis. Combining machine learning with agronomic expertise and expanding datasets will be key to making these tools practical and accessible, potentially transforming agricultural practices and global food security.

Conclusion

In summary, the leaf disease detection model, developed using convolutional neural networks (CNNs), holds immense promise for transforming agricultural practices. By leveraging deep learning techniques and large-scale datasets, the model achieves impressive accuracy in identifying plant diseases from images. The integration of advanced technologies, including CNN architectures and user-friendly smartphone applications, empowers farmers with real-time diagnostic tools for timely interventions and crop protection. However, addressing challenges related to dataset diversity, model interpretability, and scalability remains crucial. Collaborative efforts among researchers, agronomists, and technology developers will drive the adoption of leaf disease detection systems, contributing to sustainable agriculture and global food security

References

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Copyright

Copyright © 2025 B. Aravind, B. Sai Sankeerth, A. Venkata Sai Tarun Reddy, Shiva Kumar Reddy Basawada, Siddhanth Bang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.